Text Report Analysis to Identify Opportunities for Optimizing Target Selection for Chest Radiograph Artificial Intelligence Models
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10278-023-00927-5.pdf
Reference30 articles.
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2. Gipson J, Tang V, Seah J, et al. Diagnostic accuracy of a commercially available deep-learning algorithm in supine chest radiographs following trauma. British Journal of Radiology. British Institute of Radiology; 2022;95(1134). https://doi.org/10.1259/BJR.20210979
3. Seah JCY, Tang CHM, Buchlak QD, et al. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. Lancet Digit Health. Elsevier Ltd; 2021;3(8):e496–e506. https://doi.org/10.1016/S2589-7500(21)00106-0.
4. Irvin J, Rajpurkar P, Ko M, et al. CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison. www.aaai.org. Accessed February 1, 2019.
5. Schwartz TM, Hillis SL, Sridharan R, et al. Interpretation time for screening mammography as a function of the number of computer-aided detection marks. Journal of Medical Imaging. Society of Photo-Optical Instrumentation Engineers; 2020;7(2):1. https://doi.org/10.1117/1.JMI.7.2.022408.
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